Recalculation of power costs for the CANDU reactor

"NYO-9716."AT(30-1)-207

Lossless Digitally-Assisted Windowed Current Sensing for Solar Photovoltaic Applications

Sensing current across a sense resistor adds to the total power loss and decreases efficiency. This work demonstrates the use of a current sense amplifier that measures current across a length of wire so as to minimize power loss. Current Sense Amplifiers for solar photovoltaic applications require measuring a wide range of current (e.g. 1-10A), yet a high resolution is needed for certain applications such as maximum power point tracking (MPPT) and differential power processing (DPP). The windowing technique has been adopted in this work, so as to measure the current with high resolution and yet to be able to cover the entire range of current as well. The use of PSOC 4 by Cypress offers a cost effective one chip solution to all current sensing requirements for solar photovoltaic applications. This is achieved by having a nearly $4 cost reduction from previous methods, and yet attaining under 1% error in current measurements.Ope

Hybrid-inspired coolant additive for radiator application

Due to the increasing demand in the industrial application, nanofluids has attracted a considerable attention of researchers in the last few decades. Nanocellulose with water (W) and Ethylene Glycol (EG) addition to coolant for car radiator application exhibits beneficial properties to improve the efficiency of the radiator. Improved efficiency leads to more compact design of the radiator and increase the durability of the engine. The focus of the present work is to investigate the performance of mono or hybrid metal oxide such as Al2O3 and TiO2 with or without plant base extracted nanocellulose (CNC) with varying concentration as a better heat transfer nanofluid as compared to distilled water as radiator coolant. Therefore, the objective of the present work is to improve and create a new radiator coolant based on aluminium oxide and CNC with readily available coolants (EG) and to investigate the erosion of CNC coolant on automotive radiator. The scope of the present work is CNC dispersed in base fluid of EG and W with 60:40 ratio. The volume concentrations such as 0.1, 0.5, and 0.9% of tested samples have been used for further investigation. Comparative heat transfer performance of prepared nanofluids and convection thermal transport fluid has been investigated in the automotive radiator test rig under two different circumstances i.e., with and without the influence of draft fan. Obtained result reveals that heat transfer coefficient, convective heat transfer, Reynolds number, Nusselt number has proportional relation with volumetric flow rate. The highest absorption peak have been noticed in 0.9% volume concentration of TiO2, Al2O3, CNC, Al2O3/TiO2, and Al2O3/CNC nanofluids which indicate the better stability of nanofluids suspension. Better thermal conductivity improvement have been observed for Al2O3 nanofluids in all mono nanofluids followed by CNC and TiO2 nanofluids respectively. Thermal conductivity of Al2O3/CNC hybrid nanofluids with 0.9% volume concentration has been found to be superior to Al2O3/TiO2 hybrid nanofluids. Al2O3/CNC hybrid nanofluid dominates over other mono and hybrid nanofluids in terms of viscosity at all volume concentrations. CNC nanofluids (all volume concentrations) exhibited the highest specific heat capacity than other mono nanofluids. Additionally, in both the hybrid nanofluids, Al2O3/CNC showed the lowest specific heat capacity. The optimized volume concentration from statistical analytical tool was found to be 0.5%. Nanofluid volume concentration with 0.5% (CNC/Al2O3 and CNC) was selected as thermal transport fluid to be compared with convectional EG-W mixture. The experiment result shows that experimental heat transfer coefficient, convective heat transfer, Reynolds number, Nusselt number has proportional relation with volumetric flow rate

A survey of federated learning from data perspective in the healthcare domain : Challenges, methods, and future directions

Recent advances in deep learning (DL) have shown that data-driven insights can be used in smart healthcare applications to improve the quality of life for patients. DL needs more data and diversity to build a more accurate system. To satisfy these requirements, more data need to be pooled at the centralized server to train the model deeply, but the process of pooling faces privacy and regulatory challenges. To settle them, the concept of sharing model learning rather than sharing data through federated learning (FL) is proposed. FL creates a more reliable system without transferring data to the server, resulting in the right system with stronger security and access rights to data that protect privacy. This research aims to (1) provide a literature review and an in-depth study on the roles of FL in the fields of healthcare; (2) highlight the effectiveness of current challenges facing standardized FL, including statistical data heterogeneity, privacy and security concerns, expensive communications, limited resources, and efficiency; and (3) present lists of open research challenges and recommendations for future FL for the academic and industrial sectors in telemedicine and remote healthcare applications. An extensive review of the literature on FL from a data-centric perspective was conducted. We searched the Science Direct, IEEE Xplore, and PubMed databases for publications published between January 2018 and January 2023. A new crossover matching between the approaches that solve or mitigate all types of skewed data has been proposed to open up opportunities to other researchers. In addition, a list of various applications was organized by learning application task types such as prediction, diagnosis, and classification. We think that this study can serve as a helpful manual for academics and industry professionals, giving them guidance and important directions for future studies

Vibration signal for bearing fault detection using random forest

Based on the chosen properties of an induction motor, a random forest (RF) classifier, a machine learning technique, is examined in this study for bearing failure detection. A time-varying actual dataset with four distinct bearing states was used to evaluate the suggested methodology. The primary objective of this research is to evaluate the bearing defect detection accuracy of the RF classifier. First, run four loops that cycle over each feature of the data frame corresponding to the daytime index to determine the bearing states. There were 465 repetitions of the inner race fault and the roller element fault in test 1, 218 repetitions of the outer race fault in test 2, and 6324 repetitions of the outer race in test 3. Secondly, the task is to find the data for the typical bearing data procedure to differentiate between normal and erroneous data. Out of 3 tests, (22-23) % normal data was obtained since every bearing beginning to degrade usually exhibits some form of a spike in many locations, or the bearing is not operating at its optimum speed. Thirdly, to display and comprehend the data in a 2D and 3D environment, Principal Component Analysis (PCA) is performed. Fourth, the RF algorithm classifier recognized the data frame's actual predictions, which were 99% correct for normal bearings, 97% accurate for outer races, 94% accurate for inner races, and 97% accurate for roller element faults. It is thus concluded that the proposed algorithm is capable to identify the bearing faults

Construction novel highly active photocatalytic H2 evolution over noble-metal-free trifunctional Cu3P/CdS nanosphere decorated g-C3N4 nanosheet

Hydrogen energy possesses immense potential in developing a green renewable energy system. However, a significant problem still exists in improving the photocatalytic H2 production activity of metal-free graphitic carbon nitride (g-C3N4) based photocatalysts. Here is a novel Cu3P/CdS/g-C3N4 ternary nanocomposite for increasing photocatalytic H2 evolution activity. In this study, systematic characterizations have been carried out using techniques like X-ray diffraction (XRD), scanning electron microscopy (SEM), high resolution transmission electron microscopy (HR-TEM), Raman spectra, UV–Vis diffuse reflectance spectroscopy, X-ray photoelectron spectroscopy (XPS), surface area analysis (BET), electrochemical impedance (EIS), and transient photocurrent response measurements. Surprisingly, the improved 3CP/Cd-6.25CN photocatalyst displays a high H2 evolution rate of 125721 μmol h−1 g−1. The value obtained exceeds pristine g-C3N4 and Cu3P/CdS by 339.8 and 7.6 times, respectively. This could be the maximum rate of hydrogen generation for a g–C3N4–based ternary nanocomposite ever seen when exposed to whole solar spectrum and visible light (λ > 420 nm). This research provides fresh perspectives on the rational manufacture of metal-free g-C3N4 based photocatalysts that will increase the conversion of solar energy. By reusing the used 3CP/Cd/g-C3N4 photocatalyst in five consecutive runs, the stability of the catalyst was investigated, and their individual activity in the H2 production activity was assessed. To comprehend the reaction mechanisms and emphasise the value of synergy between the three components, several comparison systems are built

An Approach for the optimization of thermal conductivity and viscosity of hybrid (Graphene Nanoplatelets, GNPs : Cellulose Nanocrystal, CNC) nanofluids using Response Surface Methodology (RSM)

Response surface methodology (RSM) is used in this study to optimize the thermal characteristics of single graphene nanoplatelets and hybrid nanofluids utilizing the miscellaneous design model. The nanofluids comprise graphene nanoplatelets and graphene nanoplatelets/cellulose nanocrystal nanoparticles in the base fluid of ethylene glycol and water (60:40). Using response surface methodology (RSM) based on central composite design (CCD) and mini tab 20 standard statistical software, the impact of temperature, volume concentration, and type of nanofluid is used to construct an empirical mathematical formula. Analysis of variance (ANOVA) is applied to determine that the developed empirical mathematical analysis is relevant. For the purpose of developing the equations, 32 experiments are conducted for second-order polynomial to the specified outputs such as thermal conductivity and viscosity. Predicted estimates and the experimental data are found to be in reasonable arrangement. In additional words, the models could expect more than 85% of thermal conductivity and viscosity fluctuations of the nanofluid, indicating that the model is accurate. Optimal thermal conductivity and viscosity values are 0.4962 W/m-K and 2.6191 cP, respectively, from the results of the optimization plot. The critical parameters are 50 °C, 0.0254%, and the category factorial is GNP/CNC, and the relevant parameters are volume concentration, temperature, and kind of nanofluid. From the results plot, the composite is 0.8371. The validation results of the model during testing indicate the capability of predicting the optimal experimental conditions

Enhancing Heat Transfer in Compact Automotive Engines using Hybrid Nano Coolants

This research aimed to compare the performance of a reduced-scale automotive radiator using single nano coolant (CNC and CuO) and its hybrid nano coolant (CNC and CuO nanoparticles) to enhance heat transmission. Three ratios of 70:30, 80:20, and 90:10 of hybrid nano coolants was tested. UV Vis stability characterization of the nanofluids showed that all samples were highly stable for up to 30 days. A modest concentration (0.01 vol per cent) of the hybrid nano coolant was shown to efficiently increase the heat transfer rate of a reduced-size automobile radiator, demonstrating that the heat transfer behaviour of the nano coolant was reliant on the particle volume percentage. The results show the potential use of hybrid nano coolants in increasing heat transfer efficiency, decreasing cooling system size by up to 71 percent, and thus lowering fuel consumption; these benefits have significant implications for developing more efficient cooling systems in various industrial applications. The experimental findings showed that 80:20 exhibited a significant amount of improvement in thermal properties. The consistency of the low volume concentration of hybrid nano coolants throughout the experiment is further evidence of their promise as a practical substitute for conventional cooling media in the compact size of an automotive engine cooling system

Transgenic Expression of Entire Hepatitis B Virus in Mice Induces Hepatocarcinogenesis Independent of Chronic Liver Injury

Hepatocellular carcinoma (HCC), the third leading cause of cancer deaths worldwide, is most commonly caused by chronic hepatitis B virus (HBV) infection. However, whether HBV plays any direct role in carcinogenesis, other than indirectly causing chronic liver injury by inciting the host immune response, remains unclear. We have established two independent transgenic mouse lines expressing the complete genome of a mutant HBV (“preS2 mutant”) that is found at much higher frequencies in people with HCC than those without. The transgenic mice show evidence of stress in the endoplasmic reticulum (ER) and overexpression of cyclin D1 in hepatocytes. These mice do not show any evidence of chronic liver injury, but by 2 years of age a majority of the male mice develop hepatocellular neoplasms, including HCC. Unexpectedly, we also found a significant increase in hepatocarcinogenesis independent of necroinflammation in a transgenic line expressing the entire wildtype HBV. As in the mutant HBV mice, HCC was found only in aged—2-year-old—mice of the wildtype HBV line. The karyotype in all the three transgenic lines appears normal and none of the integration sites of the HBV transgene in the mice is near an oncogene or tumor suppressor gene. The significant increase of HCC incidence in all the three transgenic lines—expressing either mutant or wildtype HBV—therefore argues strongly that in absence of chronic necroinflammation, HBV can contribute directly to the development of HCC

Interview with Benedict Anderson

On October 1, 2008, Benedict Anderson presented a talk at Columbia University in which he discussed his upcoming book, a biography of the Chinese-Indonesian journalist Kwee Thiam Tjing. Having found a book of Kwee’s writings in a second-hand bookshop in Indonesia in 1962, Anderson describes his surprise that no one could identify the pseudonymous author, who wrote what Anderson considers to be “the greatest piece of prose written in the first half of the 20th century by anybody in Indonesia.” For years after Kwee’s death, Anderson explains, details of the journalist’s life and work were forgotten. It was only recently that Anderson was himself able to write about the author, in the process considering the role of cosmopolitanism in the life of the colonial subject